library("RColorBrewer")
library(Signac)
library(Seurat)
library(GenomicRanges)
library(future)
#library(SeuratWrappers)
library(harmony)
library(EnsDb.Hsapiens.v86)
library(stringr)
library(dplyr)
library(ggplot2)
library(patchwork)
library(kableExtra)
library(tidyverse)
set.seed(173)
# Paths
path_to_obj <- here::here("~/Documents/multiome_tonsil_Lucia/results/R_objects/11.tonsil_multiome_integrated_without_doublets_normalized.rds")
path_to_markers<-here::here("~/Documents/multiome_tonsil_Lucia/results/tables/tonsil_markers_no_doublets_05.csv")
# Thresholds
max_doublet_score_rna <- 0.3
tonsil_wnn_without_doublet <- readRDS(path_to_obj)
tonsil_markers_05<-read_csv(path_to_markers)
## New names:
## * `` -> ...1
## Rows: 8409 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): ...1, gene
## dbl (6): p_val, avg_log2FC, pct.1, pct.2, p_val_adj, cluster
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Resolution 0.05
tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC) %>% write.csv(.,file=paste0("~/Documents/multiome_tonsil_Lucia/results/tables/", "top10_tonsil_markers_no_doublets_05.csv"))
tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC) %>% write.csv(.,file=paste0("~/Documents/multiome_tonsil_Lucia/results/tables/", "top5_tonsil_markers_no_doublets_05.csv"))
top5_tonsil_markers_05<-tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)
top7_tonsil_markers_05<-tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 7, wt = avg_log2FC)
top10_tonsil_markers_05<-tonsil_markers_05 %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
df_top5<-as.data.frame(top5_tonsil_markers_05)
kbl(df_top5,caption = "Table of the top 5 marker of each cluster resolution 0.005") %>%
kable_paper("striped", full_width = F)
| …1 | p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | cluster | gene |
|---|---|---|---|---|---|---|---|
| BANK1 | 0 | 3.107693 | 0.993 | 0.428 | 0 | 0 | BANK1 |
| COL19A1 | 0 | 2.508477 | 0.619 | 0.107 | 0 | 0 | COL19A1 |
| ARHGAP24 | 0 | 2.262911 | 0.951 | 0.456 | 0 | 0 | ARHGAP24 |
| ADAM28 | 0 | 2.214714 | 0.815 | 0.338 | 0 | 0 | ADAM28 |
| MARCH1 | 0 | 2.113625 | 0.876 | 0.384 | 0 | 0 | MARCH1 |
| INPP4B | 0 | 3.210898 | 0.909 | 0.120 | 0 | 1 | INPP4B |
| FYB1 | 0 | 3.085975 | 0.933 | 0.131 | 0 | 1 | FYB1 |
| LEF1 | 0 | 2.824967 | 0.622 | 0.067 | 0 | 1 | LEF1 |
| IL6ST | 0 | 2.674320 | 0.736 | 0.152 | 0 | 1 | IL6ST |
| IL7R | 0 | 2.663301 | 0.715 | 0.081 | 0 | 1 | IL7R |
| HMGB2 | 0 | 2.958610 | 0.932 | 0.151 | 0 | 2 | HMGB2 |
| TUBA1B | 0 | 2.887534 | 0.949 | 0.224 | 0 | 2 | TUBA1B |
| H2AFZ | 0 | 2.613753 | 0.947 | 0.252 | 0 | 2 | H2AFZ |
| TOP2A | 0 | 2.497528 | 0.796 | 0.021 | 0 | 2 | TOP2A |
| STMN1 | 0 | 2.462446 | 0.917 | 0.086 | 0 | 2 | STMN1 |
| AC023590.11 | 0 | 2.944159 | 0.984 | 0.213 | 0 | 3 | AC023590.1 |
| MAML31 | 0 | 2.785879 | 0.834 | 0.216 | 0 | 3 | MAML3 |
| RAPGEF51 | 0 | 2.620607 | 0.930 | 0.163 | 0 | 3 | RAPGEF5 |
| AC104170.11 | 0 | 2.606568 | 0.820 | 0.110 | 0 | 3 | AC104170.1 |
| AFF21 | 0 | 2.377730 | 0.926 | 0.186 | 0 | 3 | AFF2 |
| CCL5 | 0 | 3.634883 | 0.742 | 0.026 | 0 | 4 | CCL5 |
| AOAH1 | 0 | 3.252688 | 0.791 | 0.158 | 0 | 4 | AOAH |
| GNLY | 0 | 2.901600 | 0.189 | 0.004 | 0 | 4 | GNLY |
| NKG7 | 0 | 2.662984 | 0.559 | 0.011 | 0 | 4 | NKG7 |
| DTHD11 | 0 | 2.536666 | 0.515 | 0.044 | 0 | 4 | DTHD1 |
| IGHG1 | 0 | 5.974981 | 0.648 | 0.243 | 0 | 5 | IGHG1 |
| IGLC1 | 0 | 6.002027 | 0.835 | 0.415 | 0 | 5 | IGLC1 |
| IGKC | 0 | 6.156868 | 0.964 | 0.892 | 0 | 5 | IGKC |
| IGHA1 | 0 | 6.496845 | 0.678 | 0.408 | 0 | 5 | IGHA1 |
| IGLC2 | 0 | 6.068831 | 0.901 | 0.718 | 0 | 5 | IGLC2 |
| SLC8A11 | 0 | 4.322800 | 0.764 | 0.041 | 0 | 6 | SLC8A1 |
| LYZ | 0 | 3.986308 | 0.714 | 0.008 | 0 | 6 | LYZ |
| PLXDC2 | 0 | 3.382540 | 0.721 | 0.005 | 0 | 6 | PLXDC2 |
| S100A9 | 0 | 3.338779 | 0.282 | 0.020 | 0 | 6 | S100A9 |
| SPRR3 | 0 | 3.476821 | 0.173 | 0.014 | 0 | 6 | SPRR3 |
| LINC013741 | 0 | 4.400369 | 0.976 | 0.059 | 0 | 7 | LINC01374 |
| LINC01478 | 0 | 3.957180 | 0.920 | 0.008 | 0 | 7 | LINC01478 |
| FAM160A11 | 0 | 3.698304 | 0.928 | 0.010 | 0 | 7 | FAM160A1 |
| RUNX23 | 0 | 3.453543 | 0.992 | 0.186 | 0 | 7 | RUNX2 |
| TCF41 | 0 | 3.753866 | 0.992 | 0.534 | 0 | 7 | TCF4 |
df_top7<-as.data.frame(top7_tonsil_markers_05)
df_mark<-as.data.frame(tonsil_markers_05)
kbl(df_top7,caption = "Table of the top 5 marker of each cluster resolution 0.005") %>%
kable_paper("striped", full_width = F)
| …1 | p_val | avg_log2FC | pct.1 | pct.2 | p_val_adj | cluster | gene |
|---|---|---|---|---|---|---|---|
| BANK1 | 0 | 3.107693 | 0.993 | 0.428 | 0 | 0 | BANK1 |
| COL19A1 | 0 | 2.508477 | 0.619 | 0.107 | 0 | 0 | COL19A1 |
| ARHGAP24 | 0 | 2.262911 | 0.951 | 0.456 | 0 | 0 | ARHGAP24 |
| ADAM28 | 0 | 2.214714 | 0.815 | 0.338 | 0 | 0 | ADAM28 |
| MARCH1 | 0 | 2.113625 | 0.876 | 0.384 | 0 | 0 | MARCH1 |
| AC120193.1 | 0 | 2.109600 | 0.755 | 0.278 | 0 | 0 | AC120193.1 |
| ZDHHC14 | 0 | 1.951733 | 0.589 | 0.227 | 0 | 0 | ZDHHC14 |
| INPP4B | 0 | 3.210898 | 0.909 | 0.120 | 0 | 1 | INPP4B |
| FYB1 | 0 | 3.085975 | 0.933 | 0.131 | 0 | 1 | FYB1 |
| LEF1 | 0 | 2.824967 | 0.622 | 0.067 | 0 | 1 | LEF1 |
| IL6ST | 0 | 2.674320 | 0.736 | 0.152 | 0 | 1 | IL6ST |
| IL7R | 0 | 2.663301 | 0.715 | 0.081 | 0 | 1 | IL7R |
| ST8SIA1 | 0 | 2.637194 | 0.567 | 0.071 | 0 | 1 | ST8SIA1 |
| BCL11B | 0 | 2.632781 | 0.826 | 0.086 | 0 | 1 | BCL11B |
| HMGB2 | 0 | 2.958610 | 0.932 | 0.151 | 0 | 2 | HMGB2 |
| TUBA1B | 0 | 2.887534 | 0.949 | 0.224 | 0 | 2 | TUBA1B |
| H2AFZ | 0 | 2.613753 | 0.947 | 0.252 | 0 | 2 | H2AFZ |
| TOP2A | 0 | 2.497528 | 0.796 | 0.021 | 0 | 2 | TOP2A |
| STMN1 | 0 | 2.462446 | 0.917 | 0.086 | 0 | 2 | STMN1 |
| HIST1H4C | 0 | 2.409732 | 0.844 | 0.331 | 0 | 2 | HIST1H4C |
| TUBB | 0 | 2.306446 | 0.927 | 0.173 | 0 | 2 | TUBB |
| AC023590.11 | 0 | 2.944159 | 0.984 | 0.213 | 0 | 3 | AC023590.1 |
| MAML31 | 0 | 2.785879 | 0.834 | 0.216 | 0 | 3 | MAML3 |
| RAPGEF51 | 0 | 2.620607 | 0.930 | 0.163 | 0 | 3 | RAPGEF5 |
| AC104170.11 | 0 | 2.606568 | 0.820 | 0.110 | 0 | 3 | AC104170.1 |
| AFF21 | 0 | 2.377730 | 0.926 | 0.186 | 0 | 3 | AFF2 |
| LHFPL21 | 0 | 2.343496 | 0.795 | 0.179 | 0 | 3 | LHFPL2 |
| MYO1E1 | 0 | 2.300488 | 0.963 | 0.335 | 0 | 3 | MYO1E |
| CCL5 | 0 | 3.634883 | 0.742 | 0.026 | 0 | 4 | CCL5 |
| AOAH1 | 0 | 3.252688 | 0.791 | 0.158 | 0 | 4 | AOAH |
| GNLY | 0 | 2.901600 | 0.189 | 0.004 | 0 | 4 | GNLY |
| NKG7 | 0 | 2.662984 | 0.559 | 0.011 | 0 | 4 | NKG7 |
| DTHD11 | 0 | 2.536666 | 0.515 | 0.044 | 0 | 4 | DTHD1 |
| PLCB11 | 0 | 2.494851 | 0.476 | 0.048 | 0 | 4 | PLCB1 |
| GZMK | 0 | 2.480087 | 0.537 | 0.011 | 0 | 4 | GZMK |
| IGHG1 | 0 | 5.974981 | 0.648 | 0.243 | 0 | 5 | IGHG1 |
| IGHGP | 0 | 5.913659 | 0.501 | 0.128 | 0 | 5 | IGHGP |
| IGHG3 | 0 | 5.894529 | 0.705 | 0.272 | 0 | 5 | IGHG3 |
| IGLC1 | 0 | 6.002027 | 0.835 | 0.415 | 0 | 5 | IGLC1 |
| IGKC | 0 | 6.156868 | 0.964 | 0.892 | 0 | 5 | IGKC |
| IGHA1 | 0 | 6.496845 | 0.678 | 0.408 | 0 | 5 | IGHA1 |
| IGLC2 | 0 | 6.068831 | 0.901 | 0.718 | 0 | 5 | IGLC2 |
| SLC8A11 | 0 | 4.322800 | 0.764 | 0.041 | 0 | 6 | SLC8A1 |
| LYZ | 0 | 3.986308 | 0.714 | 0.008 | 0 | 6 | LYZ |
| PLXDC2 | 0 | 3.382540 | 0.721 | 0.005 | 0 | 6 | PLXDC2 |
| S100A9 | 0 | 3.338779 | 0.282 | 0.020 | 0 | 6 | S100A9 |
| LCN2 | 0 | 3.326250 | 0.167 | 0.009 | 0 | 6 | LCN2 |
| LRMDA | 0 | 3.205879 | 0.718 | 0.007 | 0 | 6 | LRMDA |
| SPRR3 | 0 | 3.476821 | 0.173 | 0.014 | 0 | 6 | SPRR3 |
| LINC013741 | 0 | 4.400369 | 0.976 | 0.059 | 0 | 7 | LINC01374 |
| LINC01478 | 0 | 3.957180 | 0.920 | 0.008 | 0 | 7 | LINC01478 |
| FAM160A11 | 0 | 3.698304 | 0.928 | 0.010 | 0 | 7 | FAM160A1 |
| RUNX23 | 0 | 3.453543 | 0.992 | 0.186 | 0 | 7 | RUNX2 |
| P2RY141 | 0 | 3.366014 | 0.952 | 0.068 | 0 | 7 | P2RY14 |
| ZFAT2 | 0 | 3.361999 | 0.948 | 0.242 | 0 | 7 | ZFAT |
| TCF41 | 0 | 3.753866 | 0.992 | 0.534 | 0 | 7 | TCF4 |
#install.packages("htmlwidgets", type = "binary")
#install.packages("DT", type = "binary")
DT::datatable(df_top7)
DT::datatable(df_mark)
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
markerGenes <- unique(tonsil_markers_05$gene)
geneSym <- ifelse(test = !grepl('NA', markerGenes),
yes = sub('.*?-', '', markerGenes),
no = sub('-.*', '', markerGenes))
dot.10 <- DotPlot(tonsil_wnn_without_doublet, features = unique(top10_tonsil_markers_05$gene),cols = 'RdBu', cluster.idents = T) + theme(axis.text.x = element_text( size = 10, vjust = 0.8, hjust = 0.8)) + scale_x_discrete(labels= geneSym)+ggtitle("res 0.05 top 10 of each cluster")
dot.5 <- DotPlot(tonsil_wnn_without_doublet, features = unique(top5_tonsil_markers_05$gene),cols = 'RdBu', cluster.idents = T) + theme(axis.text.x = element_text( size = 10, vjust = 0.8, hjust = 0.8)) + scale_x_discrete(labels= geneSym)+ggtitle("res 0.05 top 5 of each cluster")
dot.10 +
coord_flip() +
theme(axis.title = element_blank(), axis.text.y = element_text(size = 5))
dot.5 +
coord_flip() +
theme(axis.title = element_blank(), axis.text.y = element_text(size = 7))
top7mark_cluster0<-top7_tonsil_markers_05[["gene"]][1:7]
top7mark_cluster1<-top7_tonsil_markers_05[["gene"]][8:14]
top7mark_cluster2<-top7_tonsil_markers_05[["gene"]][15:21]
top7mark_cluster3<-top7_tonsil_markers_05[["gene"]][22:28]
top7mark_cluster4<-top7_tonsil_markers_05[["gene"]][29:35]
top7mark_cluster5<-top7_tonsil_markers_05[["gene"]][36:42]
top7mark_cluster6<-top7_tonsil_markers_05[["gene"]][43:49]
top7mark_cluster7<-top7_tonsil_markers_05[["gene"]][50:56]
markers_gg <- function(x){purrr::map(x, function(x) {
p <- FeaturePlot(
tonsil_wnn_without_doublet,
features = x,
reduction = "wnn.umap",
pt.size = 0.1
)
p
})}
m<-c("PRDM1","XBP1","IRF4","MEF2B","BCL6")
DZ<-c("SUGCT", "CXCR4", "AICDA")
LZ<- c("CD83","BCL2A1")
GC<- c("MEF2B", "BCL6","IRF4")
PC<- c("PRDM1","SLAMF7", "MZB1", "FKBP11")
markers_gg(top7mark_cluster0)
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naive_markers<-c("CD79A", "CD79B", "BLNK")
memory_markers<-c("CD27")
markers_gg(naive_markers)
## [[1]]
##
## [[2]]
##
## [[3]]
markers_gg(memory_markers)
## [[1]]
markers_gg(c("MS4A1","NT5E"))
## [[1]]
##
## [[2]]
IL6ST: naive CD4 T-cel
CCR7, CD62L, and CD45RA
cd4<- c("CCR7")
markers_gg(top7mark_cluster1)
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markers_gg(cd4)
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markers_gg(top7mark_cluster2)
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markers_gg(GC)
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markers_gg(DZ)
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markers_gg(top7mark_cluster3)
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markers_gg(LZ)
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ccl5:cd8t cell, nk
AOAH: NK GNLY:NK
markers_gg(top7mark_cluster4)
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markers_gg("KIR2DL4")
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markers_gg(top7mark_cluster5)
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markers_gg( "MYO1E")
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markers_gg(PC)
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markers_gg(top7mark_cluster6)
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monocytes_markers<-c("LYZ","S100A8")
markers_gg(monocytes_markers)
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markers_gg(top7mark_cluster7)
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DimPlot(tonsil_wnn_without_doublet, reduction = "wnn.umap", label = TRUE, pt.size = 0.5)
cell.num <- table(Idents(tonsil_wnn_without_doublet))
cell.num
##
## 0 1 2 3 4 5 6 7
## 27168 17606 8469 7281 2656 2218 742 250
cell.num <- table(Idents(tonsil_wnn_without_doublet))
cell.num
##
## 0 1 2 3 4 5 6 7
## 27168 17606 8469 7281 2656 2218 742 250
new.cluster.ids <- c("Naive/MBC", "Naive CD4 T-celL","GC/DZ", "GC/LZ", "NK T-cell", "PC", "Monocytes","NI")
names(new.cluster.ids) <- levels(tonsil_wnn_without_doublet)
tonsil_wnn_without_doublet <- RenameIdents(tonsil_wnn_without_doublet, new.cluster.ids)
DimPlot(tonsil_wnn_without_doublet, reduction = "wnn.umap", label = TRUE, pt.size = 0.5)
MARKERS
Immature B cells express CD19, CD 20, CD34, CD38, and CD45R, T-cell receptor/CD3 complex (TCR/CD3 complex)
DZ: SUGCT, CXCR4, AICDA
LZ: CD83, BCL2A1
GC total: MEF2B, BCL6, IRF4
PC: PRDM1, SLAMF7, MZB1, FKBP11
canonical_bcell_markers <-c("CD34", "CD38", "CD19")
monocytes_markers<-c("LYZ","S100A8")
naive_markers<-c("CD79A", "CD79B", "BLNK")
bib_Bcell_markers<-c("CD19","CR2","MS4A1","RALGPS2","CD79A")
bib_Tcell_markers<-c("CD3E","CD4","CD8A","FOXP3","IL17A")
markers_bcell<-c("BANK1","ARHGAP24","ADAM28","MARCH1","RAPGEF5","AFF2","RGS13","LPP","IGHG1","IGLC1","SLC8A1","LYZ","PLXDC2","FAM160A1","IGHA1","IGLC2", "SETBP1","ENTPD1","COL19A1","CCSER1")
markers_tcell<-c("INPP4B","FYB1","LEF1","IL7R","IL6ST","CCL5","GNLY","NKG7","DTHD1","RUNX2", "FOXP3","CD8A","IL17A","CD2")
markers_gg(naive_markers)
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##
## [[2]]
##
## [[3]]
markers_gg(bib_Bcell_markers)
## [[1]]
##
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##
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##
## [[5]]
CD8+ T cell markers:“CD3D”, “CD8A” NK cell markers:“GNLY”, “NKG7”
markers_gg(bib_Tcell_markers)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
naive_mem_bcell<-c("BANK1", "FCER2")
cd4_tcell<-c("CD3D", "IL7R")
dz_gc_bcell<-c("MKI67", "TOP2A")
lz_gc_bcell<-c("MARCKSL1", "RGS13", "LMO2", "CCDC88A")
cytotoxic<-c("GNLY", "NKG7", "GZMK", "CD8A")
memory_bcell<-c( "FCRL4", "FCRL5", "PLAC8", "SOX5")
pc<-c("IGHG1", "IGHA1", "JCHAIN", "XBP1")
myeloid<-c("LYZ", "S100A8")
poor_q_doublets <-c("FDCSP", "CLU", "CXCL13", "CR2")
doublet_proliferative_tcell<-c("MT2A", "CD3D", "TRAC", "PCNA")
Unk<-c("PTPRCAP", "CD37", "CD74")
PDC<-c("PTCRA", "LILRA4", "IRF7")
markers_gg(naive_mem_bcell)
## [[1]]
##
## [[2]]
markers_gg(cd4_tcell)
## [[1]]
##
## [[2]]
markers_gg(dz_gc_bcell)
## [[1]]
##
## [[2]]
markers_gg(lz_gc_bcell)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
markers_gg(cytotoxic)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
Cytotoxic T cells are effector cells that destroy virus-infected cells, tumor cells, and tissue grafts that exist in the cytosol, or contiguous nuclear compartment. The cells are also known as CD8+ T
markers_gg(pc)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
markers_gg(myeloid)
## [[1]]
##
## [[2]]
markers_gg(poor_q_doublets)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
markers_gg(doublet_proliferative_tcell)
## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
Proliferating cell nuclear antigen (PCNA)
markers_gg(Unk)
## [[1]]
##
## [[2]]
##
## [[3]]
markers_gg(PDC)
## [[1]]
##
## [[2]]
##
## [[3]]
markers_gg("FOXO1")
## [[1]]
“NFKB1”: CLUSTER 2
markers_gg( "MYO1E")
## [[1]]
FeaturePlot(
tonsil_wnn_without_doublet,
features = "doublet_scores",
reduction = "wnn.umap",
pt.size = 0.1
)
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
tonsil_wnn_without_doublet <- CellCycleScoring(tonsil_wnn_without_doublet, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
## Warning: The following features are not present in the object: MLF1IP, not
## searching for symbol synonyms
## Warning: The following features are not present in the object: FAM64A, HN1, not
## searching for symbol synonyms
head(tonsil_wnn_without_doublet[[]])
## lib_name_barcode orig.ident
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 BCLL_15_T_1_AAACAGCCAGCAACCT-1 SeuratProject
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 BCLL_15_T_1_AAACAGCCAGCTTAGC-1 SeuratProject
## BCLL_15_T_1_AAACAGCCATTATGGT-1 BCLL_15_T_1_AAACAGCCATTATGGT-1 SeuratProject
## BCLL_15_T_1_AAACATGCAAATTGCT-1 BCLL_15_T_1_AAACATGCAAATTGCT-1 SeuratProject
## BCLL_15_T_1_AAACATGCAGGACACA-1 BCLL_15_T_1_AAACATGCAGGACACA-1 SeuratProject
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 BCLL_15_T_1_AAACATGCAGGCCAAA-1 SeuratProject
## nCount_RNA nFeature_RNA nCount_ATAC
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 2938 1493 14475
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 5693 2527 14100
## BCLL_15_T_1_AAACAGCCATTATGGT-1 2391 1408 6201
## BCLL_15_T_1_AAACATGCAAATTGCT-1 5899 2580 1984
## BCLL_15_T_1_AAACATGCAGGACACA-1 5427 2582 15009
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 2377 1121 12678
## nFeature_ATAC nucleosome_signal
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 6069 0.9178862
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 5960 0.7073955
## BCLL_15_T_1_AAACAGCCATTATGGT-1 2759 0.7165354
## BCLL_15_T_1_AAACATGCAAATTGCT-1 967 0.7112069
## BCLL_15_T_1_AAACATGCAGGACACA-1 6360 0.8482014
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 5233 0.5805921
## nucleosome_percentile TSS.enrichment
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0.88 4.890692
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 0.44 3.685808
## BCLL_15_T_1_AAACAGCCATTATGGT-1 0.47 6.287590
## BCLL_15_T_1_AAACATGCAAATTGCT-1 0.45 7.088911
## BCLL_15_T_1_AAACATGCAGGACACA-1 0.78 5.591753
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0.15 5.826994
## TSS.percentile tss.level percent.mt percent_ribo
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0.31 High 9.326072 5.616065
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 0.03 High 3.249605 4.180573
## BCLL_15_T_1_AAACAGCCATTATGGT-1 0.84 High 3.554998 9.912171
## BCLL_15_T_1_AAACATGCAAATTGCT-1 0.94 High 8.442109 7.119851
## BCLL_15_T_1_AAACATGCAGGACACA-1 0.65 High 6.467662 6.854616
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0.73 High 13.378208 19.099706
## nCount_peaks nFeature_peaks library_name
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 7403 6067 BCLL_15_T_1
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 7876 6443 BCLL_15_T_1
## BCLL_15_T_1_AAACAGCCATTATGGT-1 3011 2632 BCLL_15_T_1
## BCLL_15_T_1_AAACATGCAAATTGCT-1 1109 1053 BCLL_15_T_1
## BCLL_15_T_1_AAACATGCAGGACACA-1 7473 6202 BCLL_15_T_1
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 5989 4857 BCLL_15_T_1
## donor_id sex age age_group hospital assay
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACAGCCATTATGGT-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACATGCAAATTGCT-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACATGCAGGACACA-1 BCLL-15-T male 33 young_adult CIMA multiome
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 BCLL-15-T male 33 young_adult CIMA multiome
## barcodes doublet_scores
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 AAACAGCCAGCAACCT-1 0.020
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 AAACAGCCAGCTTAGC-1 0.024
## BCLL_15_T_1_AAACAGCCATTATGGT-1 AAACAGCCATTATGGT-1 0.062
## BCLL_15_T_1_AAACATGCAAATTGCT-1 AAACATGCAAATTGCT-1 0.103
## BCLL_15_T_1_AAACATGCAGGACACA-1 AAACATGCAGGACACA-1 0.138
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 AAACATGCAGGCCAAA-1 0.019
## predicted_doublets peaks.weight RNA.weight
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 FALSE 0.4765878 0.5234122
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 FALSE 0.4848371 0.5151629
## BCLL_15_T_1_AAACAGCCATTATGGT-1 FALSE 0.4136732 0.5863268
## BCLL_15_T_1_AAACATGCAAATTGCT-1 FALSE 0.5659107 0.4340893
## BCLL_15_T_1_AAACATGCAGGACACA-1 FALSE 0.3559236 0.6440764
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 FALSE 0.5752736 0.4247264
## wsnn_res.0.005 wsnn_res.0.01 seurat_clusters
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0 0 0
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 2 2 3
## BCLL_15_T_1_AAACAGCCATTATGGT-1 1 1 1
## BCLL_15_T_1_AAACATGCAAATTGCT-1 3 4 6
## BCLL_15_T_1_AAACATGCAGGACACA-1 1 1 4
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0 0 0
## sub.cluster_0.25 sub.cluster0_0.5 is_doublet
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0 0_4 FALSE
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 2 2 FALSE
## BCLL_15_T_1_AAACAGCCATTATGGT-1 1_0 1 FALSE
## BCLL_15_T_1_AAACATGCAAATTGCT-1 4 4 FALSE
## BCLL_15_T_1_AAACATGCAGGACACA-1 1_6 1 FALSE
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0 0_0 FALSE
## wsnn_res.0.05 wsnn_res.0.75 wsnn_res.0.075
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0 1 0
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 3 4 3
## BCLL_15_T_1_AAACAGCCATTATGGT-1 1 2 1
## BCLL_15_T_1_AAACATGCAAATTGCT-1 6 17 6
## BCLL_15_T_1_AAACATGCAGGACACA-1 4 15 4
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0 0 0
## S.Score G2M.Score Phase old.ident
## BCLL_15_T_1_AAACAGCCAGCAACCT-1 0.04267816 -0.04808593 S Naive/MBC
## BCLL_15_T_1_AAACAGCCAGCTTAGC-1 0.05184659 -0.15125381 S GC/LZ
## BCLL_15_T_1_AAACAGCCATTATGGT-1 -0.09496973 -0.04192763 G1 Naive CD4 T-celL
## BCLL_15_T_1_AAACATGCAAATTGCT-1 -0.12384198 -0.08988897 G1 Monocytes
## BCLL_15_T_1_AAACATGCAGGACACA-1 -0.07576004 -0.15292531 G1 NK T-cell
## BCLL_15_T_1_AAACATGCAGGCCAAA-1 0.02528023 -0.04173396 S Naive/MBC
print(x = tonsil_wnn_without_doublet[["pca"]],
dims = 1:10,
nfeatures = 5)
## PC_ 1
## Positive: MKI67, TOP2A, MYBL1, STMN1, AC023590.1
## Negative: BCL2, FYB1, TC2N, INPP4B, BCL11B
## PC_ 2
## Positive: FYB1, INPP4B, BCL11B, TC2N, CD247
## Negative: TCF4, FCRL5, COL19A1, AUTS2, CD83
## PC_ 3
## Positive: MAML3, AC104170.1, LHFPL2, FGD6, CCDC88A
## Negative: COL19A1, TOP2A, ASPM, UBE2C, DLGAP5
## PC_ 4
## Positive: AC104170.1, RAPGEF5, FGD6, AC023590.1, AFF2
## Negative: PLXDC2, LRMDA, DOCK5, LYZ, CSF2RA
## PC_ 5
## Positive: DERL3, MZB1, XBP1, FKBP11, CFAP54
## Negative: BACH2, SLC8A1, LPP, PLXDC2, LRMDA
## PC_ 6
## Positive: KIF14, PLK1, CENPE, DEPDC1, HMMR
## Negative: MCM4, PCNA, DTL, HSP90AB1, CLSPN
## PC_ 7
## Positive: NELL2, LINC00861, PDE3B, LEF1, IL7R
## Negative: DRAIC, KSR2, TOX2, ICA1, PTPN14
## PC_ 8
## Positive: SPRR3, LCN2, S100A9, KRT13, MUC4
## Negative: MCTP1, TOX2, DRAIC, LYZ, SLC8A1
## PC_ 9
## Positive: ACTG1, RPS26, CDC20, CFL1, TCL1A
## Negative: AC105402.3, BRIP1, FKBP5, PDE4D, HIPK2
## PC_ 10
## Positive: BACH2, TCL1A, GAB1, IGHM, P2RY14
## Negative: FCRL4, DNAH8, AL355076.2, ATP8B4, MIR155HG
PCNA: Proliferating cell nuclear antigen
# Visualize the distribution of cell cycle markers across
RidgePlot(tonsil_wnn_without_doublet, features = c("PCNA", "TOP2A", "MCM6", "MKI67"), ncol = 2)
## Picking joint bandwidth of 0.0706
## Picking joint bandwidth of 0.056
## Picking joint bandwidth of 0.0482
## Picking joint bandwidth of 0.0468
tonsil_wnn_without_doublet <- RunPCA(tonsil_wnn_without_doublet, features = c(s.genes, g2m.genes))
## Warning in PrepDR(object = object, features = features, verbose = verbose): The
## following 30 features requested have not been scaled (running reduction without
## them): TYMS, MCM2, UNG, PRIM1, UHRF1, MLF1IP, RFC2, RPA2, RAD51AP1, SLBP, UBR7,
## POLD3, MSH2, RAD51, TIPIN, DSCC1, BLM, CASP8AP2, USP1, CHAF1B, FAM64A, HN1,
## RANGAP1, NCAPD2, PSRC1, LBR, CTCF, G2E3, CBX5, CENPA
## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.
## PC_ 1
## Positive: MKI67, TOP2A, TPX2, NUSAP1, CENPE, GTSE1, CDK1, CENPF, AURKB, ANLN
## HMGB2, HMMR, BUB1, KIF11, DLGAP5, BIRC5, NDC80, UBE2C, CDCA2, TUBB4B
## RRM2, KIF23, CDCA3, ECT2, CDCA8, CKAP2L, KIF2C, CCNB2, TTK, HJURP
## Negative: POLA1, CCNE2, MCM5, MCM6, CDC6, NASP, EXO1, GINS2, GAS2L3, DTL
## CDC45, ATAD2, CKAP2, TMPO, HELLS, WDR76, RRM1, CKAP5, ANP32E, GMNN
## FEN1, MCM4, KIF20B, PCNA, NEK2, BRIP1, TACC3, E2F8, CDCA7, CLSPN
## PC_ 2
## Positive: MCM4, CLSPN, DTL, HELLS, CDC45, PCNA, GINS2, CDC6, BRIP1, WDR76
## EXO1, MCM6, POLA1, ATAD2, FEN1, CCNE2, MCM5, RRM2, E2F8, GMNN
## NASP, RRM1, CDCA7, SMC4, TMPO, HMGB2, ANP32E, KIF11, NUSAP1, MKI67
## Negative: AURKA, GAS2L3, CDC20, HMMR, CENPE, UBE2C, NEK2, DLGAP5, CENPF, CCNB2
## KIF23, CDCA8, TPX2, CDCA3, BUB1, TTK, TOP2A, HJURP, CKAP2L, GTSE1
## CKS2, CKAP2, CDC25C, ECT2, NUF2, KIF2C, CKAP5, TUBB4B, BIRC5, AURKB
## PC_ 3
## Positive: ANLN, E2F8, CDC25C, RRM2, NDC80, KIF11, TMPO, ECT2, CKAP2L, HJURP
## BRIP1, KIF23, CDCA2, EXO1, GTSE1, TTK, ATAD2, CDK1, BUB1, CKAP5
## MKI67, SMC4, KIF20B, POLA1, RRM1, KIF2C, NUSAP1, TACC3, TOP2A, NUF2
## Negative: CDC20, CCNB2, CKS2, CKS1B, NEK2, BIRC5, GINS2, NASP, HMGB2, TUBB4B
## DTL, ANP32E, MCM5, MCM4, MCM6, CDCA7, CENPF, AURKA, CDC6, CDCA3
## UBE2C, GMNN, HMMR, PCNA, CDC45, CDCA8, TPX2, DLGAP5, GAS2L3, FEN1
## PC_ 4
## Positive: FEN1, RRM1, E2F8, ANP32E, PCNA, CKS1B, RRM2, AURKB, CDCA3, TUBB4B
## CKS2, NASP, HMGB2, GMNN, BIRC5, UBE2C, NDC80, CDK1, MKI67, TACC3
## GTSE1, KIF2C, NUSAP1, HJURP, TOP2A, CKAP2L, CLSPN, ANLN, CDCA8, GINS2
## Negative: GAS2L3, POLA1, DTL, CKAP2, KIF20B, CKAP5, NEK2, MCM6, BRIP1, HELLS
## CDC45, EXO1, ECT2, CDCA7, TTK, CDC6, AURKA, CENPE, CCNB2, HMMR
## CENPF, CDCA2, WDR76, ATAD2, CDC20, SMC4, CCNE2, DLGAP5, MCM4, NUF2
## PC_ 5
## Positive: MCM5, NASP, KIF20B, CKAP5, CKAP2, ANP32E, GINS2, FEN1, CDC25C, TTK
## KIF11, CDCA7, POLA1, ANLN, MCM6, CDCA2, PCNA, BUB1, RRM1, HJURP
## KIF2C, MCM4, HELLS, KIF23, NDC80, SMC4, ECT2, CKS2, CDK1, CKAP2L
## Negative: CCNE2, TACC3, CDC6, NEK2, DTL, EXO1, CDC20, CCNB2, BIRC5, CDC45
## TMPO, ATAD2, HMGB2, CLSPN, CDCA3, CENPF, E2F8, WDR76, BRIP1, TUBB4B
## HMMR, CKS1B, RRM2, GAS2L3, AURKB, TOP2A, DLGAP5, NUF2, MKI67, AURKA
tonsil_wnn_without_doublet <- RunUMAP(object = tonsil_wnn_without_doublet,
nn.name = "weighted.nn",
reduction.name = "wnn.umap",
reduction.key = "wnnUMAP_" )
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 17:16:13 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:16:16 Commencing smooth kNN distance calibration using 1 thread
## 17:16:19 Initializing from normalized Laplacian + noise
## 17:16:23 Commencing optimization for 200 epochs, with 2156298 positive edges
## 17:17:07 Optimization finished
DimPlot(tonsil_wnn_without_doublet,
reduction = "wnn.umap",
pt.size = 0.1, label = T, split.by = "age_group")
DimPlot(tonsil_wnn_without_doublet,
reduction = "wnn.umap",
pt.size = 0.1, label = T)